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Fractal-based analysis of optical coherence tomography data to quantify retinal tissue damage

Overview of attention for article published in BMC Bioinformatics, September 2014
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Article details
Title
Fractal-based analysis of optical coherence tomography data to quantify retinal tissue damage
Published in
BMC Bioinformatics, September 2014
DOI 10.1186/1471-2105-15-295
Pubmed ID
Authors
Abstract

The sensitivity of Optical Coherence Tomography (OCT) images to identify retinal tissue morphology characterized by early neural loss from normal healthy eyes is tested by calculating structural information and fractal dimension. OCT data from 74 healthy eyes and 43 eyes with type 1 diabetes mellitus with mild diabetic retinopathy (MDR) on biomicroscopy was analyzed using a custom-built algorithm (OCTRIMA) to measure locally the intraretinal layer thickness. A power spectrum method was used to calculate the fractal dimension in intraretinal regions of interest identified in the images. ANOVA followed by Newman-Keuls post-hoc analyses were used to test for differences between pathological and normal groups. A modified p value of <0.001 was considered statistically significant. Receiver operating characteristic (ROC) curves were constructed to describe the ability of each parameter to discriminate between eyes of pathological patients and normal healthy eyes.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Canada 2 4%
Unknown 45 96%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 8 17%
Student > Ph. D. Student 7 15%
Student > Master 4 9%
Student > Doctoral Student 3 6%
Student > Bachelor 3 6%
Other 8 17%
Unknown 14 30%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 13 28%
Computer Science 4 9%
Neuroscience 2 4%
Chemistry 2 4%
Engineering 2 4%
Other 8 17%
Unknown 16 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 September 2014.
All research outputs
#17,725,418
of 22,761,738 outputs
Outputs from BMC Bioinformatics
#5,928
of 7,273 outputs
Outputs of similar age
#159,577
of 237,234 outputs
Outputs of similar age from BMC Bioinformatics
#83
of 107 outputs
Altmetric has tracked 22,761,738 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 237,234 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.